Neural Network Model Assessment for Side-Channel Analysis, by Guilherme Perin and Baris Ege and Lukasz Chmielewski

Leakage assessment of cryptographic implementations with side-channel analysis relies on two important assumptions: leakage model and the number of side-channel traces. In the context of profiled side-channel attacks, having these assumptions correctly defined is a sufficient first step to evaluate the security of a crypto implementation with template attacks. This method assumes that the features (leakages or points of interest) follow a univariate or multi-variate Gaussian distribution for the estimation of the probability density function. When trained machine learning or neural network models are employed as classifiers for profiled attacks, a third assumption must be taken into account that it the correctness of the trained model or learning parameters. It was already proved that convolutional neural networks have advantages for side-channel analysis like bypassing trace misalignments and defeating first-order masking countermeasures in software implementations. However, if this trained model is incorrect and the test classification accuracy is close to random guessing, the correctness of the two first assumptions (number of traces and leakage model) will be insufficient and the security of the target under evaluation can be overestimated. This could lead to wrong conclusions in leakage certifications. One solution to verify if the trained model is acceptable relies on the identifying of input features that the neural network considers as points of interest. In this paper, we implement the assessment of neural network models by using the proposed backward propagation path method. Our method is employed during the profiling phase as a tool to verify what the neural network is learning from side-channel traces and to support the optimization of hyper-parameters. The method is tested against masked AES implementation. One of the main results highlights the importance of L2 regularization for the automated points of interest selection from a neural network.